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Sahbi SIDHOM (LORIA & Université de Lorraine, France)

Veille scientifique et technique : modèle d'observation et d’analyse issu de l'intelligence collective en nanosciences . Scientific and technical monitoring: observation and analysis model from the collective intelligence in nanoscience. Sahbi SIDHOM (LORIA & Université de Lorraine, France)

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Sahbi SIDHOM (LORIA & Université de Lorraine, France)

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  1. Veille scientifique et technique : modèle d'observation et d’analyse issu de l'intelligence collective en nanosciences .Scientific and technical monitoring: observation and analysis model from the collective intelligence in nanoscience Sahbi SIDHOM(LORIA & Université de Lorraine, France) & Philippe LAMBERT (C’NanoGrand Est & Université de Lorraine, France) eMails: sahbi.sidhom@univ-lorraine.fr, philippe.lambert@univ-lorraine.fr

  2. Outline Concepts & Validations: monitoring process 1. Cognitive Observations 2. Adaptabilities 3. Information Analysis & Validations Approach: knowledge process 4. KnowledgeOrganization & Management (KO&M) Applications: (toward) decision-making process 5. Case study in context of NanoMetrologycluster S. Sidhom (guest speaker) January 22th, 2015

  3. Concepts et Validations: 1. Cognitive Observations 2. Adaptabilities 3. Information Analysis & Validations I. S. Sidhom (guest speaker) January 22th, 2015

  4. Starting point Activities/ Experiences Knowledge Organization Representation Resource Knowledge S. Sidhom (guest speaker) January 22th, 2015

  5. Case Study n°1 • SEMUSDI Project (INSA France) • Ressource • Difficulties in « multimedia » representation • SIMBAD project (INA France) • Ressource • (multimedia) Representation • Difficulties in « KnowledgeOrganization » representation S. Sidhom (guest speaker) January 22th, 2015

  6. Cognitive observations Analysis Grid : Audiovisual indexing in I.N.A Studies in ORTF & SEMA (1970): Analysis method of current movies • Human indexing and pre-processing • Saves time into the audiovisual analysis Analysis Grid of audiovisual (1990): • Position: Backgrounds & shot by shot • Camera movements • People & Places identified for action • Distinction between Image & Sound S. Sidhom (guest speaker) January 22th, 2015

  7. Illustration: Analysis Grid (1/2) NATURE DE PRODUCTION : PRODUCTION PROPRE (CODE 01) descripteurs principaux : DE Descripteurs thématiques et géographiques, personnes morales, personnes physiques évoquées. descripteurs secondaires : DES Descripteurs séquences et images. Nom des villes si images réutilisables. Nom des personnes si visibles à l'image. résumé court : RES / chapeau Chapeau précisant la forme du sujet et situant l'événement dans le temps et dans l'espace résumé développé : SEQ / résumé Description par séquence du sujet. S. Sidhom (guest speaker) January 22th, 2015

  8. Illustration: Analysis Grid (2/2) A NATURE DE PRODUCTION : PRODUCTION PROPRE (CODE 01) Titre propre : Un lac venu de l'espace : le cratère du Nouveau Québec Titre collection: France 2 Documentaire Descripteurs principaux:météorite; lac (Nouveau Québec); Québec; expédition (scientifique); chercheur Producteurs (aff) : FRANCE 2, 1995 Nature de production (aff) : PRODUCTION PROPRE B Chapeau :Ce documentaire retrace les travaux menés par une équipe de chercheursdans le NouveauQuébec, afin d'expliquer la présence d'un lac qui se serait formé suite à la chute d'une météorite. Résumé : La chute d'une météoritevenue de l'espace a créé un lacdans la Toundra du Nouveau Québec. Celui-ci mesure 2,7 km de diamètre, 267 mètres de profondeur et son cratère s'étend sur 3 km. Une équipe multidisciplinaire de chercheurs, dirigée par le professeur Michel BOUCHARD, de l'université de Montréal, a monté une expéditionsur le site afin de répondre aux questions essentielles que se pose le monde scientifique sur l'origine extra-terrestre de la météore et la datation de son impact. Le résultat de leurs études concernant l'importance de l'impact, démontre que sa puissance est équivalente à 8500 fois celle que détenait la bombe d'Hiroschima. Pour établir de façon certaine l'origine extra-terrestre de la météorite, les chercheurs tentent de retrouver des fragments d'impactible, ce qui est un succès. … S. Sidhom (guest speaker) January 22th, 2015

  9. Information Analysis & Validations A, B ? (Sidhom, 2002-2014) S. Sidhom (guest speaker) January 22th, 2015

  10. Cognitive Grammar Model Generative Grammar Theory S PI SN SV REL SP (Sidhom, 2000-2002) S. Sidhom (guest speaker) January 22th, 2015

  11. Adaptabilities? s.INA to s’.Nano In INA context, the S syntactic structure is: (Sidhom, 2002) In Nanometrology context, the S’ syntactic structure is: (Sidhom & Lambert, 2014) S. Sidhom (guest speaker) January 22th, 2015

  12. Information Analysis & Validations A, B ? S. Sidhom (guest speaker) January 22th, 2015

  13. Approach: 4. KnowledgeOrganization & Management (KO&M) II. S. Sidhom (guest speaker) January 22th, 2015

  14. Knowledge Organization (KO)? López-Huertas (2008) addresses current research questions in the field of KO - knowledge organization - under two broad areas: • a demand for quality: umbrellatermfor research questions related to social (social groups, ethics, and social questions) and technical (integration of structures, forms, and formats) issues and • a demand for managing emerging knowledge: special focus on work-orientedand organizational knowledge, with a special focus on “multidimensional knowledge”, ( that knowledge is multidisciplinary, interdisciplinary, transdisciplinary). Knowl. Org. revue 35(2008)No.2/No.3, pp79-81 I. C. McIlwaine and J. S. Mitchell. Preface to Special Issue. “What is Knowledge Organization” S. Sidhom (guest speaker) January 22th, 2015

  15. Knowledge Management (KM)? Knowledge Management is the name of a concept in which an enterprise consciously and comprehensively (= process) gathers, organizes, shares, and analyzes its knowledge in terms of resources, documents, and people skills. (Jeff Angus and Jeetu Patel, 1998) S. Sidhom (guest speaker) January 22th, 2015

  16. Processing: NLP and NooJ (…) S. Sidhom (guest speaker) January 22th, 2015

  17. NLP to Information Design Weak-Signal Processing in contents Key-Concepts in Resources S. Sidhom (guest speaker) January 22th, 2015

  18. Knowledge? S. Sidhom (guest speaker) January 22th, 2015

  19. Methodology and tools 1. NP Generativ Grammar 2. NooJ Processing S. Sidhom (guest speaker) January 22th, 2015

  20. Case Study n°2: ChroniSanté Project 3. http://chronisante.inist.fr S. Sidhom (guest speaker) January 22th, 2015

  21. Decision-Making Support: HCSP Pascal, Medline & PsycInfo S. Sidhom (guest speaker) January 22th, 2015

  22. Knowledge in Class relation N patient … SN1 SN3 le patient une patiente assistée SNp SN2 les patients atteints de maladies chroniques S. Sidhom (guest speaker) January 22th, 2015

  23. ). Knowledge in Fitting relation  SNmax la prise en charge de patients atteints de maladies chroniques des patients atteints de maladies chroniques SN1 des maladies chroniques SN2 S. Sidhom (guest speaker) January 22th, 2015

  24. Knowledge in Tree relation SNmax le cadre du groupe de travail sur la prise en charge des patients atteints de maladies chroniques SNg1 SNd1 le cadre du groupe de travail la prise en charge des patients atteints de maladies chroniques SNd2 SNg2 des patients atteints de maladies chroniques le groupe de travail SNd3 des maladies chroniques S. Sidhom (guest speaker) January 22th, 2015

  25. Information Design… Resources KO Pattern &Procesing S. Sidhom (guest speaker) January 22th, 2015

  26. Applications: 5. Case study in context of NanoMetrologycluster III. S. Sidhom (guest speaker) January 22th, 2015

  27. Project management by economic intelligence: Nanometrology cluster Two specialized networks in France: C’Nano (nanoscience cluster) LNE (metrology Lab.) S. Sidhom (guest speaker) January 22th, 2015

  28. Case Study n°3 : Nanometrology Custer Treatment for surveys Treatment of Responses • Statistics (closed questions) • Statistics (open questions)Automatic Language Processing surveys Projection (Social Network Analysis) S. Sidhom (guest speaker) January 22th, 2015

  29. Treatment of questions: • Online questionnaire on nanoscience • Determination skills of actors& • main motivations Strategic steering committee Actors survey respondents: • hundreds researchers in R&D research laboratories (CNRS and universities…), • Fields of Nano sciences (spintronics, photovoltaics, optoelectronics, hot plasmas, etc.). S. Sidhom (guest speaker) January 22th, 2015

  30. Decision-Making into NanoMetrology Custer Information Design Identification of weak signals: long-term “strategic” information Diagnosisin continuity: data / info + structures + NooJ resources T T A T R DEMO. S. Sidhom (guest speaker) January 22th, 2015

  31. Conclusion • Cognitive Grammar for analysis & indexing • Information Design for visualization • Knowledge Organization for Decision-Making • Key Performance Indicators (KPI): • Strategic Orientation for nanoMetrology : T+12(type of actions, topics, etc.) • Network Diagnosis into nanoMetrology cluster • Common Thematics (topics missing, links between topics, etc.) • Synergy Detection (or limits) to Community Manager • Project Anticipation in Time Monitoring process Knowledge process Economic intelligence process S. Sidhom (guest speaker) January 22th, 2015

  32. Thanks… Scientific Projects : 6th. Edt. of SIIE int. conference in 2015 (www.siie.fr) by Feb. 2015 5th. Edt. of ISKO-Maghreb int. symposium in 2015 (www.isko-maghreb.org) by nov. 2015 S. Sidhom (guest speaker) January 22th, 2015

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